Mast cells modulate transport of CD23/IgE/antigen complex across human intestinal epithelial barrier.
Bibliographic record
Abstract
BACKGROUND: Food allergy and chronic intestinal inflammation are common in western countries. The complex of antigen/IgE is taken up into the body from the gut lumen with the aid of epithelial cell-derived CD23 (low affinity IgE receptor II) that plays an important role in the pathogenesis of intestinal allergy. This study aimed to elucidate the role of mast cell on modulation of antigen/IgE complex transport across intestinal epithelial barrier. METHODS: Human intestinal epithelial cell line HT29 cell monolayer was used as a study platform. Transepithelial electric resistance (TER) and permeability to ovalbumin (OVA) were used as the markers of intestinal epithelial barrier function that were recorded in response to the stimulation of mast cell-derived chemical mediators. RESULTS: Conditioned media from naïve mast cell line HMC-1 cells or monocyte cell line THP-1 cells significantly upregulated the expression of CD23 and increased the antigen transport across the epithelium. Treatment with stem cell factor (SCF), nerve growth factor (NGF), retinoic acid (RA) or dimethyl sulphoxide (DMSO) enhanced CD23 expression in HT29 cells. Conditioned media from SCF, NGF or RA-treated HMC-1 cells, and SCF, NGF, DMSO or RA-treated THP-1 cells enhanced immune complex transport via enhancing the expression of the CD23 in HT29 cells and the release of inflammatory mediator TNF-α. Nuclear factor kappa B inhibitor, tryptase and TNF-α inhibited the increase in CD23 in HT29 cells and prevents the enhancement of epithelial barrier permeability. CONCLUSIONS: Mast cells play an important role in modulating the intestinal CD23 expression and the transport of antigen/IgE/CD23 complex across epithelial barrier. (Tu YH, Oluwole C, Struiksma S, Perdue MH, Yang PC. Mast cells modulate transport of CD23/IgE/antigen complex across human intestinal epithelial barrier.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".